Influence Maximization with Novelty Decay in Social Networks

Shanshan Feng, Xuefeng Cheng, Gao Cong, Yifeng Zeng, Yeow Meng Chee, Yanping Xiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Citations (Scopus)
3 Downloads (Pure)

Abstract

Influence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for solving this problem. However, they have not considered the impact of novelty decay on influence propagation, i.e., repeated exposures will have diminishing influence on users. In this paper, we consider the problem of influence maximization with novelty decay (IMND). We investigate the effect of novelty decay on influence propagation on real-life datasets and formulate the IMND problem. We further analyze the problem properties and propose an influence estimation technique. We demonstrate the performance of our algorithms on four social networks.
Original languageEnglish
Title of host publicationProceedings of the twenty-eighth AAAI Conference on Artificial Intelligence and the twenty-sixth Innovative Applications of Artificial Intelligence Conference
Place of PublicationPalo Alto
PublisherAssociation for the Advancement of Artificial Intelligence Press
Pages37-43
Number of pages7
ISBN (Print)9781577356615 , 9781577356776, 9781577356783, 9781577356790, 9781577356806
Publication statusPublished - Aug 2014
Externally publishedYes
EventAAAI Conference on Artificial Intelligence
Twenty-Eighth AAAI Conference on Artificial Intelligence
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Duration: 27 Jul 201431 Jul 2014

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
ISSN (Print)2159-5399

Conference

ConferenceAAAI Conference on Artificial Intelligence
Twenty-Eighth AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI
Period27/07/1431/07/14

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